Efficient hybrid image denoising scheme based on SVM classification

Routray, Sidheswar; Ray, Arun Kumar; Mishra, Chandrabhanu; Palai, G.

doi:10.1016/j.ijleo.2017.11.116

Cited by 32 publications

(17 citation statements)

References 20 publications

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“…Because of these advantages, SVM can be well-applied to pattern recognition, time series prediction, and regression estimation, among others. It is also widely used in many fields, such as handwritten character recognition, text classification, image classification, and recognition [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

et al. 2019

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The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients’ outcome prediction into two steps. The first step is to extract the key features from the patients’ many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients’ outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article’s prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.

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Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

et al. 2019

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“…Furthermore, the diversity of the application also requires the CH-CH communication of the physical layer. Many researchers often take the shortest distance algorithm [18], which has the disadvantage of high computing and system complexity. In this paper, we combine the BPNN with distributed gradient descent technology.…”

Section: Introductionmentioning

confidence: 99%

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

et al. 2020

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Wireless Sensor Network (WSN) is an essential technology for the Internet-of-Things (IoT) and intelligence-based applications. In the case of Intelligent Transportation Systems (ITS), the WSNs play an important role in safety and efficient traffic management. Therefore, there is enormous demand for energy efficient WSNs for dynamic resource allocation in vehicles and infrastructures. This work presents a Multi-Input Multi-Output (MIMO) technique model in WSNs, which addresses the Cluster Head (CH) recognition issue for MIMO sensor networks by using Back Propagation Neural Network (BPNN). The conventional CH identification suffers from a lack of location identification due to the dynamic and real-time environment. Thus, to obtain more precise positioning accuracy, the proposed work uses BPNN combined with a distributed gradient drop technique to calculate the position of the unknown CH. This reduces the distance estimation error, and the particle swarm optimization technique is further used to obtain the optimal weight and threshold of the network. The work is validated by using mathematical analysis, simulations, and comparison with existing techniques. The proposed model shows a better performance in terms of energy consumption, error rate, and computation time.

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“…In the recognition filed they have been implemented by using different databases to evaluate the proposed works. [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A novel comprehensive database for arabic and english off-line handwritten digits recognition

Abdalkafor

Awad

Alheeti

2020

IJEECS

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<span>The recognition of Arabic handwritten is received at the same interest as other Latin languages. In Optical Character Recognition (OCR), handwriting Arabic recognition is considered as one of the critical and difficult tasks in the various scientific area. The main issues of this matter were due to the lack of public Arabic handwriting databases and the cursive nature of Arabic writing. In this paper, a new benchmark database is built for the Arabic and English off-line handwritten digits Recognition. The original form is divided into three groups: Arabic digits, English digits, and word Arabic digits which written five times by 100 different academic staff and students of university writers. Our database contains 14500 images; divided into two subsets of training and testing to help researchers through evaluating and comparing results obtained from their systems. </span>

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Efficient hybrid image denoising scheme based on SVM classification

Cited by 32 publications

References 20 publications

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems

A novel comprehensive database for arabic and english off-line handwritten digits recognition

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